Xuefei Julie Wang

The paper introduces Nimbus, a deep learning model for predicting marker positivity in multiplexed imaging data using a pre-trained dataset, and shows its integration with clustering for cell subtype identification.

Polaris provides a deep learning-based pipeline for analyzing spatial transcriptomics data, improving spot detection and gene expression quantification.

The paper identifies two distinct categories of differentially expressed genes in tumor subclones based on their regulatory and functional characteristics using single-cell transcriptomic and genomic data from colorectal cancer patients.

The paper integrates single-cell RNA-seq data from multiple tissues to construct a cell atlas of hematopoietic stem and progenitor cells (HSPCs) and identifies migration patterns and signaling interactions involved in their cross-tissue differentiation.

ISSAAC-seq jointly profiles chromatin accessibility and gene expression in single nuclei, revealing cell types, regulatory elements, and chromatin dynamics in mouse cerebral cortex.

The paper examines subiculum neuron activity during a spatial working memory task, finding they encode structural features of complex environments differently than CA1 neurons.

The paper constructs a single-cell atlas of hematopoiesis and leukemogenesis, identifies leukemia subpopulations, and develops a composite index to predict clinical outcomes.

This study compares semantic representations in neural and computational systems using EEG and finds significant correlations between model outputs and early semantic processing.

The paper develops a framework for analyzing neural dynamics in EEG data using topographic analysis, providing automated temporal indices and reducing cognitive noise.